Our method is based on the LFGAA network[1]}, which has the leading effect in the zero-shot image classification network. It uses the latent attributes proposed in the LDF[2]} to increase the discrimination between categories, and proposed the object-based attribute attention \(p(x)\)  to solve the problem of equal treatment of semantic attributes in the conventional end-to-end zero-sample classification network, which led to the model's misclassification in some categories. As shown in Figure.REF , the visual features of the image output by Backbone are output as a 2K-dimensional augmented space \(\phi _{e}(x)\)  through the fully connected layer, where 2K-dimensional is composed of the artificially defined attribute space (K-dimension) and the latent attribute space (K-dimension). The artificially defined attribute prediction \(\varphi (x)\)  and the latent attribute prediction \(\sigma (x)\)  are obtained respectively:
\(\phi _{e}(x)=[\varphi (x) ; \sigma (x)], \quad \varphi (x), \sigma (x) \in \mathbb {R}^{k}\) 
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